Traditional biological infection or chemical intoxication detection occurs after agent exposure results in overt symptoms, and relies on specialized technology not appropriate for field use. New approaches allowed by high-throughput sequencing have shown the promise of pre-symptomatic detection using genomic or transcriptional expression profiles in the host. However, these approaches suffer from often prohibitively steep logistic burdens and associated costs (cold chain storage, equipment requirements, extremely qualified operators, serial sampling). Indeed, most infections presented clinically are never definitively determined etiologically, much less serially sampled. Furthermore, molecular diagnostics are rarely used until patient self-reporting and presentation of overt clinical symptoms, such as fever. Past physiological signal based early infection detection work has been almost exclusively focused on bacterial infection and largely centered upon higher time resolution analysis of body core temperature, advanced analyses of strongly-confounded signals such as heart rate variability, or social dynamics, or sensor data fusion from already symptomatic (febrile) viral-infected individuals. While progress has been made in developing techniques for signal-based early warning of bacterial infections, there appear to be no efforts in extending these techniques to possibly life-threatening viral infections or toxic chemical exposure/intoxication.